Difference between revisions of "Usage-based Data Discovery - Episode 2"
From Earth Science Information Partners (ESIP)
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= Notes = | = Notes = | ||
| + | * Use Cases: Participating: Bob, Jonathon, Beth, Ed | ||
| − | Use | + | == Best Practice Use Case == |
| − | Ed: interested in interdisciplinary, datasets used together WITHIN a given application | + | * Ed: interested in interdisciplinary, datasets used together WITHIN a given application |
| − | Mark: simple, single dataset use case + complex interdisciplinary use cases | + | * Mark: simple, single dataset use case + complex interdisciplinary use cases |
| − | Beth: dataset interconnections circles back to data transformations | + | * Beth: dataset interconnections circles back to data transformations |
| − | Irina: validation requires MANY different datasets being used together, shows up in separate of research papers that focus on the application vs. those that focus on validation | + | * Irina: validation requires MANY different datasets being used together, shows up in separate of research papers that focus on the application vs. those that focus on validation |
| − | J.B.: discovery / search process: present datasets relevant to their problem, e.g., homeowner types in more colloquial term, interpret what the user's real concern is | + | * J.B.: discovery / search process: present datasets relevant to their problem, e.g., homeowner types in more colloquial term, interpret what the user's real concern is |
| − | Adrienne (Axiom Data Science): how do we interact with users where they know what they are getting back; users expect fancy searches, but they need to be transparent (document how they got there) - show the graph! | + | * Adrienne (Axiom Data Science): how do we interact with users where they know what they are getting back; users expect fancy searches, but they need to be transparent (document how they got there) - show the graph! |
| − | B. Downs: combine with location of interest | + | * B. Downs: combine with location of interest |
| − | JB: maybe too big a tech lift at this point? | + | * JB: maybe too big a tech lift at this point? |
| − | Mark: location is relevant, sure, but how does it | + | * Mark: location is relevant, sure, but how does it |
| − | Chris: dataset used Gulf of Maine, might be useful in Puget Sound | + | * Chris: dataset used Gulf of Maine, might be useful in Puget Sound |
| − | Mark: identify desired outcome | + | * Mark: identify desired outcome |
| − | IMPACT Use Case | + | == IMPACT Use Case == |
| − | In addition to how many, what type of use cases | + | * In addition to how many, what type of use cases |
| − | Beth: Take me to the articles about the data | + | * Beth: Take me to the articles about the data |
| − | Irina: Data providers want to know about applications of their data; ask them if they know how their data are being used | + | * Irina: Data providers want to know about applications of their data; ask them if they know how their data are being used |
| − | Bob: What other data were used in conjunction with the datasets in a given climate study | + | * Bob: What other data were used in conjunction with the datasets in a given climate study |
| − | JB: datasets commonly used together (Mark: closer to Beth's use case variant) | + | * JB: datasets commonly used together (Mark: closer to Beth's use case variant) |
| − | JB: IMPACT might be a roll up of lots of datasets | + | * JB: IMPACT might be a roll up of lots of datasets |
| − | Mark: may want to contrast similar instrument, different algorithms, (Chris: or different platforms) | + | * Mark: may want to contrast similar instrument, different algorithms, (Chris: or different platforms) |
| − | Ed: | + | * Ed: roll up by Societal Benefit Area |
| − | Agenda | + | == Next Agenda == |
| − | Irina, Beth more graphs, how to make them | + | * Irina, Beth more graphs, how to make them |
| − | Adrienne: User story, aiming to create at the end | + | * Adrienne: User story, aiming to create at the end |
| − | Ed: Using graphs for discovery, esp. related datasets, practical examples | + | * Ed: Using graphs for discovery, esp. related datasets, practical examples |
| − | Mark: Take one of these use cases, work a user story end to end to see what the graph looks like, including the tools | + | * Mark: Take one of these use cases, work a user story end to end to see what the graph looks like, including the tools |
| − | Bob: examples of graphs, something about the process we are going through, getting from here to there | + | * Bob: examples of graphs, something about the process we are going through, getting from here to there |
| − | Megan: Session proposal, ESIP Collaboration Slide | + | * Megan: Session proposal, ESIP Collaboration Slide |
= New Actions = | = New Actions = | ||
Revision as of 13:54, March 19, 2020
Participants
- Chris Lynnes
- Joe Lee
- Irina Gerasimov
- Adrienne
- Megan Carter
- Ed Armstrong
- Mark Parsons
- Bob Downs
- Beth Huffer
- Jonathon Blythe
- Doug Newman
- Steve Olding
Current Actions
- Done: Lynnes - write up some text for Megan's newsletter
- Done: Lynnes set up regular meeting with ESIP G2M, some time other than 4th Thursday
- TBD - spin off a subgroup to work on use cases
- Done: Lynnes/Newman - generate a sample export of EOSDIS knowledge graph
- Done: Blythe - provide research_prov_model info on use of W3C Prov model
Agenda
- Use cases for connecting Data with Usage
- Best Practice Use Case: Which rainfall dataset is most often used for flood prediction?
- Impact Use Case: How many climate studies use TRMM 3B42 rainfall?
- <Your use case here>
- Sample graphs
- Any other business?
- Agenda for next telecon…
Notes
- Use Cases: Participating: Bob, Jonathon, Beth, Ed
Best Practice Use Case
- Ed: interested in interdisciplinary, datasets used together WITHIN a given application
- Mark: simple, single dataset use case + complex interdisciplinary use cases
- Beth: dataset interconnections circles back to data transformations
- Irina: validation requires MANY different datasets being used together, shows up in separate of research papers that focus on the application vs. those that focus on validation
- J.B.: discovery / search process: present datasets relevant to their problem, e.g., homeowner types in more colloquial term, interpret what the user's real concern is
- Adrienne (Axiom Data Science): how do we interact with users where they know what they are getting back; users expect fancy searches, but they need to be transparent (document how they got there) - show the graph!
- B. Downs: combine with location of interest
- JB: maybe too big a tech lift at this point?
- Mark: location is relevant, sure, but how does it
- Chris: dataset used Gulf of Maine, might be useful in Puget Sound
- Mark: identify desired outcome
IMPACT Use Case
- In addition to how many, what type of use cases
- Beth: Take me to the articles about the data
- Irina: Data providers want to know about applications of their data; ask them if they know how their data are being used
- Bob: What other data were used in conjunction with the datasets in a given climate study
- JB: datasets commonly used together (Mark: closer to Beth's use case variant)
- JB: IMPACT might be a roll up of lots of datasets
- Mark: may want to contrast similar instrument, different algorithms, (Chris: or different platforms)
- Ed: roll up by Societal Benefit Area
Next Agenda
- Irina, Beth more graphs, how to make them
- Adrienne: User story, aiming to create at the end
- Ed: Using graphs for discovery, esp. related datasets, practical examples
- Mark: Take one of these use cases, work a user story end to end to see what the graph looks like, including the tools
- Bob: examples of graphs, something about the process we are going through, getting from here to there
- Megan: Session proposal, ESIP Collaboration Slide
